Most Odoo teams hit the same wall: standard pivot views and graph widgets are tied to one model at a time, no single pane crosses sales, inventory, and accounting, and every "just add one more metric" request becomes a developer ticket. Native Odoo boards (the spreadsheet_dashboard layer in Enterprise, or per-app graph/pivot views in Community) are useful but rigid — they can't blend sale.order, stock.quant, and account.move.line into one live board, they don't let a non-technical manager ask a question in plain language, and they force a choice between pre-built views and a spreadsheet export that is stale the moment it lands. That gap is exactly what this build-to-order module fills.

We build a dedicated dashboarding layer inside your own Odoo instance: configurable dashboard records (x_dashboard plus a x_dashboard.tile line model) that each hold a set of tiles — KPI cards, trend lines, bar and pie charts, funnels, and cross-model tables — rendered in a responsive OWL client action so they feel native rather than bolted on. Each tile is powered by a saved data source that maps to real Odoo models and read_group aggregations, so figures reconcile exactly with the underlying documents instead of a copied snapshot. Heavy KPIs are backed by stored compute fields with @api.depends, and expensive roll-ups run through an automated action (ir.cron) into lightweight summary models, so a board covering a year of order lines still opens fast.

The AI-driven exploration layer is where this goes beyond charts. A managed assistant lets users type a question — "revenue by salesperson last quarter versus this one," "which SKUs are below reorder point in the north warehouse," "gross margin trend by product category" — and it maps that request to a safe, validated read_group/domain query against models the user is already permitted to see. Crucially, the AI never runs free-form SQL and never bypasses Odoo security: every query is executed through the ORM as the logged-in user, so ir.model.access.csv rules and record rules (ir.rule) fully apply, multi-company boundaries hold, and results are auditable. Answers come back as a chart or table you can pin straight onto a dashboard, with the underlying domain shown so analysts can verify the logic. We wire the assistant to your chosen LLM endpoint (your key, your data-handling policy) or an on-prem option, and you decide which models and fields are ever in scope.

Because this is a build, it fits your reality: your KPI definitions, your fiscal calendar, your warehouse and analytic-account structure, your access matrix, and your Odoo edition. We deliver an installable module with its own __manifest__.py, migrate cleanly across 17.0, 18.0, and 19.0, and expose board data over the standard XML-RPC/JSON-RPC API if you want to feed other systems. Dashboards, tiles, and data sources are configurable by your admins after handover — new metrics no longer require us.

Build-to-order delivery works in four steps: a scoping call to lock KPIs, models, roles, and AI boundaries; a fixed written quote; the build on a staging copy of your database with UAT sign-off; then production install with a rollback plan and a post-go-live support window. Typical delivery is one working week from confirmed scope. Pricing starts from $999 (indicative, single-company base scope); multi-company consolidation, additional data sources and integrations, deeper localization of fiscal/tax metrics, and large historical-data volumes to pre-aggregate increase the quoted scope.